David A. Freedman presents a definitive synthesis of his approach to statistical modeling and causal inference in the social sciences.

Simplifies the treatment of statistical inference focusing on how to specify and interpret models in the context of testing causal theories. Simple bivariate re

CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we

This lively and engaging book explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the technique

An accessible, contemporary introduction to the methods for determining cause and effect in the Social Sciences “Causation versus correlation has been the bas